Enhancing Land Cover Mapping and Monitoring: An Interactive and Explainable Machine Learning Approach Using Google Earth Engine

Abstract

Artificial intelligence (AI) and machine learning (ML) have been applied to solve various remote sensing problems. To fully leverage the power of AI and ML to tackle impactful remote sensing problems, it is essential to enable researchers and practitioners to understand how AI and ML models actually work and thus to improve the model performance strategically. Accurate and timely land cover maps are essential components for informed land management decision making. To address the ever-increasing need for high spatial and temporal resolution maps, this paper developed an interactive and open-source online tool, in Python, to help interpret and improve the ML models used for land cover mapping with Google Earth Engine (GEE). The tool integrates the workflow of both land cover classification and land cover change dynamics, which requires the generation of a time series of land cover maps. Three feature importance metrics are reported, including impurity-based, permutation-based, and SHAP (Shapley additive explanations) value-based feature importance. Two case studies are presented to showcase the tool’s capability and ease of use, enabling a globally accessible and free convergent application of remote sensing technologies. This tool may inspire researchers to facilitate explainable AI (XAI)-empowered remote sensing applications with GEE.